This course will cover topics in using computers to solve statistical problems. Possible subjects include: computational methods/toolkits for data wrangling, exploration, visualization and analysis with R/Python; R/python for data science; computational techniques (e.g. optimization, integration, algebra) for statistical inference; computing intensive statistical methods (e.g. bootstrapping methods, sample-size determination, Monte Carlo methods). Weekly hours: 3 Lecture hours and 1 Practicum/Lab hoursPrerequisite(s): Permission of the instructor. Note: Students may take this course more than once for credit provided that the topics covered in each offering differ substantially. Students must consult the Department to ensure that the topics covered are different.
This course will cover topics in using computers to solve statistical problems. Possible subjects include: computational methods/toolkits for data wrangling, exploration, visualization and analysis with R/Python; R/python for data science; computational techniques (e.g. optimization, integration, algebra) for statistical inference; computing intensive statistical methods (e.g. bootstrapping methods, sample-size determination, Monte Carlo methods). Weekly hours: 3 Lecture hours and 1 Practicum/Lab hoursPrerequisite(s): Permission of the instructor. Note: Students may take this course more than once for credit provided that the topics covered in each offering differ substantially. Students must consult the Department to ensure that the topics covered are different.
